Abstract

This study presents a robust predictive analytics framework using Python and SQL to reduce investment risk in digital markets. With the growing importance of digital markets, such as cryptocurrency and e-commerce, investors face significant challenges due to high market volatility, data quality issues, and the inability to predict market trends accurately. The proposed framework integrates machine learning algorithms to forecast market risks and assist in data-driven decision-making. By leveraging Python's powerful libraries (e.g., Scikit-learn, Pandas) and SQL for data management, the model processes real-time and historical market data to predict potential risks with high accuracy. The results demonstrate that the predictive model successfully enhances risk management in digital markets, offering a more adaptive and reliable approach compared to traditional risk management techniques. The model's evaluation based on key metrics, including accuracy, precision, recall, and AUC, reveals a significant improvement in risk prediction. Furthermore, this research highlights the importance of integrating market sentiment, historical trends, and trading volumes as input features to improve prediction performance. Practical recommendations for investors and financial institutions emphasize incorporating predictive analytics into existing investment strategies to mitigate risk. The study also identifies areas for future research, such as exploring more advanced machine learning models and integrating external data sources to enhance prediction accuracy further. This paper offers a significant contribution to the growing digital investment risk management field by applying predictive analytics to improve decision-making in digital markets.